论文标题
线性回归中经验先验的高维特性，误差差异未知
High-dimensional properties for empirical priors in linear regression with unknown error variance
论文作者
论文摘要
我们研究用于高维线性回归的完整贝叶斯程序。我们采用[1]中引入的数据依赖性经验先验。在他们的论文中，这些先验具有良好的后收缩特性，并且易于计算。我们的论文将其理论结果扩展到未知误差差异的情况下。在适当的稀疏性假设下，我们通过分析多元T分布来实现模型选择一致性，后部收缩率以及Bernstein von-MINS定理。
We study full Bayesian procedures for high-dimensional linear regression. We adopt data-dependent empirical priors introduced in [1]. In their paper, these priors have nice posterior contraction properties and are easy to compute. Our paper extend their theoretical results to the case of unknown error variance . Under proper sparsity assumption, we achieve model selection consistency, posterior contraction rates as well as Bernstein von-Mises theorem by analyzing multivariate t-distribution.
